Information leakage risk early warning system and method for scientific research management

Abstract

The present invention discloses an information leakage risk warning system and method for scientific research management, and relates to the technical field of electronic digital data processing. The information leakage risk warning system for scientific research management includes an initial risk assessment module, a real-time risk assessment module and a receiving risk assessment module. The present invention determines whether to make a personnel replacement reminder through the initial risk assessment module. If the personnel replacement reminder is not made, the first information leakage risk coefficient is obtained. Then the real-time risk assessment module determines whether to take transmission warning measures according to the obtained second information leakage risk coefficient and the risk assessment interval. Finally, the receiving risk assessment module determines whether to take receiving warning measures according to the obtained third information leakage risk coefficient and the receiving risk threshold when no transmission warning measures are taken, thereby improving the timeliness of the information leakage risk warning during the data transmission process, and solving the problem of untimely information leakage risk warning during the data transmission process in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to an information leakage risk early warning system and method for scientific research management. Background Art

[0002] As scientific research management becomes increasingly complex, sensitive information security issues have become a focus of attention, and early warning systems and methods for information leakage risks in scientific research management have emerged. This system aims to prevent technology leakage, internal leaks and external attacks that may occur during the scientific research process. Scientific research management involves huge data sets, such as unpublished research results and intellectual property rights, which are easy to become leak targets; information leakage risks include technology theft, malicious attacks and operational errors; early warning systems rely on monitoring, analysis and behavior modeling to prevent leaks and issue alarms. The advancement of modern big data, artificial intelligence and other technologies has also promoted the intelligent development of this system.

[0003] Existing scientific research management information security technologies mainly rely on traditional firewalls, encryption technology, and permission control to prevent information leakage. However, with the increasing complexity of scientific research activities, traditional technologies are difficult to fully respond to potential threats. Existing information leakage prevention methods usually use data leakage prevention (DLP, Data Loss Prevention) systems to identify, monitor and protect sensitive data, but there are still deficiencies in dealing with malicious behavior of internal personnel or complex network attacks. Technologies such as behavioral analysis and user operation auditing have also been introduced, but their intelligence level is low, making it difficult to conduct real-time warnings and emergency responses. More advanced systems are needed to achieve accurate leakage risk prevention and control.

[0004] For example, the patent application with publication number: CN118779882A discloses an information system risk assessment method, system, terminal device and storage medium, including: collecting unauthorized file access coefficients, monitoring equipment disconnection coefficients and external attack intensity coefficients, and processing them to generate a risk status assessment coefficient, and comparing and analyzing the risk status assessment coefficient with a pre-set risk status assessment coefficient reference threshold. If the risk status assessment coefficient is not less than the pre-set risk status assessment coefficient reference threshold, it means that even if the current scientific research management system has issued an alarm, some scientific research data may still be leaked. At this time, an alarm signal is generated and an early warning is issued to remind the staff in time.

[0005] For example, the method and device for measuring information leakage risk announced by the invention patent with announcement number: CN109670342B include: constructing a privacy information ontology tree including multiple nodes; determining known privacy information and unknown privacy information; mapping the known privacy information and the unknown privacy information to each node in the privacy information ontology tree respectively; selecting a node mapped with unknown privacy information from the privacy information ontology tree as a target node; obtaining a node that has a parent-child relationship with the target node as the current node based on the target node; then calculating the privacy leakage value of the target node based on the privacy leakage value of the current node; and determining the degree of leakage risk of the unknown privacy information based on the privacy leakage value of the target node.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, due to the difficulty in identifying abnormal behavior, insufficient data transmission preparation and data reception, there is a problem of untimely warning of information leakage risks during data transmission. Summary of the invention

[0008] The embodiments of the present application solve the problem of untimely information leakage risk warning during data transmission in the prior art by providing an information leakage risk warning system and method for scientific research management, thereby improving the timeliness of information leakage risk warning during data transmission.

[0009] An embodiment of the present application provides an information leakage risk warning system for scientific research management, including: an initial risk assessment module, a real-time risk assessment module and a receiving risk assessment module: wherein the initial risk assessment module is used to determine whether to issue a personnel replacement reminder based on the obtained personnel credit score and the credit score limit. If the personnel replacement reminder is not issued, a first information leakage risk coefficient is obtained based on the personnel credit score and the data score. The personnel credit score indicates that the data transmission personnel and the data receiving personnel are scored for dishonesty through scientific research dishonesty rules. The personnel credit score includes a first credit score and a second credit score. The data score indicates the result of assigning points to the scientific research data to be transmitted according to the data type. The first information leakage risk coefficient is used to quantify the degree of personnel leakage risk of the scientific research data to be transmitted; the real-time risk The evaluation module is used to process the acquired information transmission data, reference information transmission data and the first information leakage risk coefficient to obtain a second information leakage risk coefficient, and at the same time determine whether to take transmission warning measures based on the second information leakage risk assessment coefficient and the risk assessment interval. The second information leakage risk assessment coefficient is used to quantify the degree of leakage risk of the scientific research data to be transmitted during the transmission process; the receiving risk assessment module is used to obtain a third information leakage risk coefficient through the acquired information reception data, reference reception data, the first information leakage risk coefficient and the second information leakage risk coefficient when no transmission warning measures are taken, and determine whether to take reception warning measures based on the third information leakage risk coefficient and the receiving risk threshold. The third information leakage risk coefficient is used to quantify the degree of leakage risk of the scientific research data to be transmitted during the receiving process.

[0010] Furthermore, the specific process of obtaining the first information leakage risk coefficient is as follows: numbering the data transmission personnel and the data receiving personnel respectively and numbering the scientific research data to be transmitted; obtaining the score evaluation weight from the preset database, the score evaluation weight includes the personnel score weight and the data score weight; processing the obtained personnel credit score and data score and the corresponding score evaluation weight to obtain the first information leakage risk coefficient, and the numerical expression of the first information leakage risk coefficient is as follows:

[0011] ;

[0012] In the formula, n represents the transmission number of the scientific research data to be transmitted. , Indicates the total number of transmissions of scientific research data to be transmitted, Indicates the first credit score of the data transmission personnel, Indicates the second credit score of the data receiver. represents the data integral corresponding to the scientific research data to be transmitted for the nth transmission, represents the personnel score weight, represents the data integration weight, Represents the first information leakage risk coefficient corresponding to the scientific research data to be transmitted for the nth transmission.

[0013] Furthermore, the information transmission data includes transmission data, traffic data, error data, connection data and abnormal data; the transmission data includes transmission speed and transmission delay; the traffic data includes transmission traffic and number of data packets; the error data includes packet loss rate and number of retransmissions; the connection data includes session duration and connection frequency; the abnormal data includes number of abnormal behaviors and number of access failures; the reference information transmission data includes reference transmission data, reference traffic data, reference error data, reference connection data and reference abnormal data; the reference transmission data includes the minimum transmission speed and the maximum transmission delay; the reference traffic data includes the maximum transmission traffic and the maximum number of data packets; the reference error data includes the maximum packet loss rate and the maximum number of retransmissions; the reference connection data includes the minimum session duration and the maximum connection frequency; the reference abnormal data includes the maximum number of abnormal behaviors and the maximum number of access failures; the risk assessment interval includes a first risk interval, a second risk interval and a third risk interval.

[0014] Furthermore, the specific acquisition process of the second information leakage risk coefficient is as follows: obtain real-time risk assessment weights from a preset database, and the real-time risk assessment weights include a first real-time weight and a second real-time weight; obtain a first real-time risk coefficient based on transmission data and corresponding reference transmission data, and the first real-time risk coefficient is used to quantify the risk of transmission rate; obtain a second real-time risk coefficient based on traffic data and corresponding reference traffic data, and the second real-time risk coefficient is used to quantify the risk of traffic data; obtain a third real-time risk coefficient based on error data and corresponding reference error data, and the third real-time risk coefficient is used to quantify the risk of error data; obtain a fourth real-time risk coefficient based on connection data and corresponding reference connection data, and the fourth real-time risk coefficient is used to quantify the risk of connection duration; obtain a fifth real-time risk coefficient based on abnormal data and corresponding reference abnormal data, and the fifth real-time risk coefficient is used to quantify the risk of abnormal activities; obtain the second information leakage risk coefficient based on the first real-time risk coefficient, the second real-time risk coefficient, the third real-time risk coefficient, the fourth real-time risk coefficient, the fifth real-time risk coefficient, the first information leakage risk coefficient and the real-time risk assessment weight.

[0015] Furthermore, the information reception data includes reception time, number of received data packets, reception delay, and number of honeypot data accesses; the reference reception data includes preset reception time, number of data packets, maximum reception delay, and maximum number of honeypot data accesses; the reception risk threshold is used to judge the degree of information leakage risk in the process of receiving scientific research data to be transmitted.

[0016] Furthermore, the specific acquisition process of the third information leakage risk coefficient is as follows: obtain the receiving risk assessment weight from the preset database, and the receiving risk assessment weight includes the first receiving assessment weight, the second receiving risk assessment weight and the third receiving risk assessment weight; obtain the receiving risk coefficient according to the receiving time, the receiving delay, the number of honeypot data accesses and the reference information data, and the receiving risk coefficient is used to quantify the possibility of receiving anomalies; obtain the third information leakage risk coefficient based on the receiving risk assessment weight, the first information leakage risk coefficient, the second information leakage risk coefficient and the receiving risk coefficient.

[0017] The embodiment of the present application provides an information leakage risk warning method for scientific research management, comprising the following steps: judging whether to issue a personnel replacement reminder based on the obtained personnel credit score and the credit score limit; if the personnel replacement reminder is not issued, obtaining a first information leakage risk coefficient based on the personnel credit score and the data score; the personnel credit score indicates that the data transmission personnel and the data receiving personnel are scored for dishonesty through scientific research dishonesty rules; the personnel credit score includes a first credit score and a second credit score; the data score indicates the result of assigning points to the scientific research data to be transmitted according to the data type; the first information leakage risk coefficient is used to quantify the risk level of personnel leakage of the scientific research data to be transmitted; and The information transmission data and the first information leakage risk coefficient are processed to obtain a second information leakage risk coefficient, and at the same time, based on the second information leakage risk assessment coefficient and the risk assessment interval, it is determined whether to take transmission warning measures. The second information leakage risk assessment coefficient is used to quantify the degree of leakage risk of the scientific research data to be transmitted during the transmission process. When no transmission warning measures are taken, the third information leakage risk coefficient is obtained by obtaining the information receiving data, the reference receiving data, the first information leakage risk coefficient and the second information leakage risk coefficient. Based on the third information leakage risk coefficient and the receiving risk threshold, it is determined whether to take receiving warning measures. The third information leakage risk coefficient is used to quantify the degree of leakage risk of the scientific research data to be transmitted during the receiving process.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] 1. Determine whether to issue a personnel replacement reminder based on the obtained personnel credit points and credit point limit. If no personnel replacement reminder is issued, determine whether to take transmission warning measures based on the first information leakage risk coefficient obtained. If no transmission warning measures are taken, determine whether to take reception warning measures based on the third information leakage risk coefficient obtained. This achieves full-process warning of scientific research data to be transmitted, thereby improving the timeliness of information leakage risk warning during data transmission, and effectively solves the problem of untimely information leakage risk warning during data transmission in the prior art.

[0020] 2. By numbering the data transmission personnel and data receiving personnel respectively and numbering the scientific research data to be transmitted, and obtaining the score evaluation weight from the preset database, the obtained personnel credit points and data points and the corresponding score evaluation weight are processed to obtain the first information leakage risk coefficient, thereby reducing the risk of information leakage at the sending end, and thus achieving the transmission security of the scientific research data to be transmitted;

[0021] 3. By obtaining the receiving risk assessment weight from the preset database, and obtaining the receiving risk coefficient according to the receiving time, receiving delay, the number of honeypot data accesses and the reference information data, and then obtaining the third information leakage risk coefficient based on the receiving risk assessment weight, the first information leakage risk coefficient, the second information leakage risk coefficient and the receiving risk coefficient, the risk of information leakage at the receiving end is reduced, thereby achieving the receiving security of the scientific research data to be transmitted. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the structure of an information leakage risk warning system for scientific research management provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram of changes in a first information leakage risk coefficient and a first credit score provided in an embodiment of the present application;

[0024] Figure 3 A schematic diagram of changes in the first information leakage risk coefficient and the second credit score provided in an embodiment of the present application;

[0025] Figure 4 A schematic diagram of changes in the first information leakage risk coefficient and data integral provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiment of the present application solves the problem of untimely information leakage risk warning in the data transmission process in the prior art by providing an information leakage risk warning system and method for scientific research management. The personnel credit points and credit point limits obtained by the initial risk assessment module are used to determine whether to issue a personnel replacement reminder. If the personnel replacement reminder is not issued, the data transmission personnel and the data receiving personnel are numbered respectively and the scientific research data to be transmitted are numbered at the same time, and the point assessment weight is obtained from a preset database. Then, the obtained personnel credit points and data points and the corresponding point assessment weight are processed to obtain a first information leakage risk coefficient. Then, the real-time risk assessment module determines whether to take transmission warning measures according to the obtained second information leakage risk coefficient and the risk assessment interval. Finally, when no transmission warning measures are taken, the receiving risk assessment module obtains the receiving risk assessment weight from the preset database, and obtains the receiving risk coefficient according to the receiving time, receiving delay, the number of honeypot data accesses and the reference information data. Then, the third information leakage risk coefficient is obtained based on the receiving risk assessment weight, the first information leakage risk coefficient, the second information leakage risk coefficient and the receiving risk coefficient. Based on this and the receiving risk threshold, it is determined whether to take receiving warning measures, thereby improving the timeliness of information leakage risk warning in the data transmission process.

[0027] The technical solution in the embodiment of the present application is to solve the problem of untimely warning of information leakage risk in the above-mentioned data transmission process. The overall idea is as follows:

[0028] The initial risk assessment module is used to determine whether to issue a personnel replacement reminder. If no personnel replacement reminder is issued, the first information leakage risk coefficient is obtained. Then the real-time risk assessment module determines whether to take transmission warning measures based on the second information leakage risk coefficient and the risk assessment interval. Finally, the receiving risk assessment module determines whether to take receiving warning measures based on the third information leakage risk coefficient and the receiving risk threshold when no transmission warning measures are taken, thereby achieving the effect of improving the timeliness of information leakage risk warnings during data transmission.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0030] like Figure 1As shown, it is a structural schematic diagram of the information leakage risk warning system for scientific research management provided by the embodiment of the present application. The information leakage risk warning system for scientific research management provided by the embodiment of the present application includes: an initial risk assessment module, a real-time risk assessment module and a receiving risk assessment module: wherein the initial risk assessment module is used to determine whether to issue a personnel replacement reminder based on the obtained personnel credit score and the credit score limit. If the personnel replacement reminder is not issued, a first information leakage risk coefficient is obtained based on the personnel credit score and the data score. The personnel credit score indicates that the data transmission personnel and the data receiving personnel are scored for dishonesty through the scientific research dishonesty behavior rules. The personnel credit score includes a first credit score and a second credit score. The data score indicates the result of assigning points to the scientific research data to be transmitted according to the data type. The first information leakage risk coefficient is used to quantify the risk level of personnel leakage of the scientific research data to be transmitted; the real-time risk assessment module and the receiving risk assessment module are used to quantify the risk level of personnel leakage of the scientific research data to be transmitted; The risk assessment module is used to process the acquired information transmission data, reference information transmission data and the first information leakage risk coefficient to obtain the second information leakage risk coefficient, and at the same time, determine whether to take transmission warning measures based on the second information leakage risk assessment coefficient and the risk assessment interval. The transmission warning is used to prevent the leakage of scientific research data to be transmitted during the transmission process. The second information leakage risk assessment coefficient is used to quantify the degree of leakage risk of scientific research data to be transmitted during the transmission process; the receiving risk assessment module is used to obtain the third information leakage risk coefficient through the acquired information receiving data, reference receiving data, the first information leakage risk coefficient and the second information leakage risk coefficient when no transmission warning measures are taken, and determine whether to take reception warning measures based on the third information leakage risk coefficient and the receiving risk threshold. The third information leakage risk coefficient is used to quantify the degree of leakage risk of scientific research data to be transmitted during the receiving process.

[0031] In this embodiment, the system realizes all-round monitoring and leakage risk prevention of scientific research data from personnel, data to transmission and receiving links by introducing a phased, multi-level risk assessment and early warning mechanism, thereby improving the security level of scientific research data and the timeliness of information leakage risk warning during data transmission.

[0032] It should be added that the initial risk assessment module includes a personnel credit assessment unit, a data risk assessment unit and a sending risk assessment unit; the personnel credit assessment unit: is used to obtain the corresponding personnel credit points based on the dishonest behavior data of the data transmission personnel and the data receiving personnel. When the obtained personnel credit points are not less than the credit point limit, the obtained personnel credit points will be transmitted to the data risk assessment unit, otherwise a personnel replacement reminder will be issued; the data risk assessment unit: is used to obtain data points based on the transmission type and importance of the scientific research data to be transmitted, and transmit the obtained data points to the sending risk assessment unit. The transmission types include public data, internal data, sensitive data and highly sensitive data; the sending risk assessment unit: is used to obtain the first information leakage risk coefficient based on the personnel credit points and data points, and conduct leakage risk assessment on the sending of the scientific research data to be transmitted.

[0033] Personnel credit points (not less than 0) are set according to the rules for investigating and handling scientific research misconduct. When the administrator enters the researcher's violation of the relevant scientific research misconduct investigation and handling rules into the system, the system automatically uses a counter to accumulate personnel credit points for the relevant researcher.

[0034] The data integral (not less than 0) varies depending on the transmission type. The transmission type is usually divided into public data, internal data, sensitive data and highly sensitive data, but different institutions or fields may have specific classification standards; for example, if the transmission type is public data, the corresponding data integral is 0.2, if the transmission type is internal data, the corresponding data integral is 0.5, if the transmission type is sensitive data, the corresponding data integral is 0.8, if the transmission type is highly sensitive data, the corresponding data integral is 1.0, the higher the data integral, the more important the scientific research data to be transmitted; the initial risk assessment module can accurately prevent information leakage through the dual assessment of personnel credit and data importance, and provide comprehensive protection for the safe transmission of scientific research data.

[0035] Specifically, the credit score limit is set according to the rules of the specific scientific research management agency. In a specific embodiment, if the scientific research management agency sets the credit score limit to 5, when the credit score of a researcher exceeds the credit score limit of 5, the researcher cannot participate in the transmission of the scientific research data to be transmitted.

[0036] Furthermore, the specific process of obtaining the first information leakage risk coefficient is as follows: number the data transmission personnel and data receiving personnel respectively, and number the scientific research data to be transmitted; obtain the score evaluation weight from the preset database, and the score evaluation weight includes the personnel score weight and the data score weight; process the obtained personnel credit points and data points and the corresponding score evaluation weight to obtain the first information leakage risk coefficient, and the numerical expression of the first information leakage risk coefficient is as follows:

[0037] ;

[0038] In the formula, n represents the transmission number of the scientific research data to be transmitted. , Indicates the total number of transmissions of scientific research data to be transmitted, Indicates the first credit score of the data transmission personnel, Indicates the second credit score of the data receiver. represents the data integral corresponding to the scientific research data to be transmitted for the nth transmission, represents the personnel score weight, represents the data integration weight, Represents the first information leakage risk coefficient corresponding to the scientific research data to be transmitted for the nth transmission.

[0039] In this embodiment, the algorithm combines the first credit score, the second credit score, the data score and the score evaluation weight for comprehensive analysis to obtain the first information leakage risk coefficient, wherein the first credit score, the second credit score and the data score are all positively correlated with the first information leakage risk coefficient, wherein the first credit score and the second credit score are both affected by the credit score limit, the first credit score represents the personnel credit score of the data transmission personnel, and the second credit score represents the personnel credit score of the data receiving personnel; as the first credit score, the second credit score and the data score increase, the first information leakage risk coefficient also increases, indicating that the risk of information leakage at the sending end also increases, therefore generally speaking, higher data scores often require corresponding data transmission personnel and data receiving personnel with lower personnel credit scores.

[0040] Specifically, assume that the personnel score weight is 0.7 and the data score weight is 0.3; Figure 2 A schematic diagram of the change of the first information leakage risk coefficient and the first credit score provided in an embodiment of the present application, assuming that the second credit score is 5 and the data score is 2 in the example; Figure 3 A schematic diagram of changes in the first information leakage risk coefficient and the second credit score provided in an embodiment of the present application, assuming in the figure that the first credit score is 5 and the data score is 2; Figure 4 A schematic diagram of the change of the first information leakage risk coefficient and the data score provided in the embodiment of the present application. In the example, it is assumed that the first credit score is 5 and the second credit score is 5; Figure 2 , Figure 3 and Figure 4It can be seen that the images of the first credit score, the second credit score, the data score and the first information leakage risk coefficient all show an upward trend, among which the growth rates of the first credit score, the second credit score and the first information leakage risk coefficient are higher than the growth rates of the data score and the first information leakage risk coefficient, indicating that under the current score evaluation weights, the evaluation of the initial risk assessment module focuses more on the personnel credit score, and has higher credit requirements for scientific researchers involved in the transmission of scientific research data to be transmitted. Therefore, the higher the personnel credit score and the higher the data score, the greater the risk of information leakage at the sending end. Through the dual evaluation of personnel and data, the security of the scientific research data to be transmitted is fully guaranteed at the sending end, and the risk of information leakage at the sending end is reduced.

[0041] Specifically, the personnel score weight is the weight corresponding to the personnel credit score in the preset database, which represents the numerical value of the influence of the personnel credit score on the first information leakage risk coefficient. When used, the weight corresponding to the personnel credit score can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the first credit score corresponding to the data transmission personnel and the weight corresponding to the first information leakage risk coefficient preset in the preset database form a mapping set, and the real-time first credit score is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship is a one-to-one correspondence. In this example, its value range is [0, 1].

[0042] Specifically, in this example, the sum of the data score weight and the personnel score weight is 1.

[0043] Furthermore, the information transmission data includes transmission data, traffic data, error data, connection data and abnormal data; the transmission data includes transmission speed and transmission delay; the traffic data includes transmission traffic and the number of data packets; the error data includes packet loss rate and number of retransmissions; the connection data includes session duration and connection frequency; the abnormal data includes the number of abnormal behaviors and the number of access failures; the reference information transmission data includes reference transmission data, reference traffic data, reference error data, reference connection data and reference abnormal data; the reference transmission data includes the minimum transmission speed and the maximum transmission delay; the reference traffic data includes the maximum transmission traffic and the maximum number of data packets; the reference error data includes the maximum packet loss rate and the maximum number of retransmissions; the reference connection data includes the minimum session duration and the maximum connection frequency; the reference abnormal data includes the maximum number of abnormal behaviors and the maximum number of access failures; the risk assessment interval includes a first risk interval, a second risk interval and a third risk interval.

[0044] In this embodiment, the network detection tool Wireshark is used to obtain transmission data, number of data packets, error data, connection data, and number of access failures in real time, NetFlow is used to count transmission traffic in real time, and Snort is used to monitor and record abnormal behavior and count the number of abnormal behaviors. Through the acquisition and analysis of information transmission data and reference information transmission data, the system's real-time monitoring and risk assessment capabilities during the transmission of scientific research data to be transmitted are enhanced.

[0045] Specifically, the reference transmission data is obtained from a preset database. In a specific embodiment, the minimum transmission speed and the maximum transmission delay are set according to the transmission network benchmark data of the scientific research data to be transmitted. For example, if the transmission speed in the transmission network benchmark data is 100Mbps, the minimum transmission speed can be set to 80% of the transmission speed in the network benchmark data, that is, 80Mbps; if the average delay is 50ms in a network with a transmission speed of 100Mbps, the transmission delay is set to twice the average delay, that is, 100ms.

[0046] Specifically, the reference traffic data is obtained from a preset database. In a specific embodiment, the maximum transmission traffic and the maximum number of data packets are set according to historical traffic data. For example, if the normal transmission traffic peak is 500MB / hour, 500MB / hour is set as the maximum transmission traffic; if 10,000 data packets are transmitted per second, the maximum number of data packets is set to 10,000.

[0047] Specifically, the reference error data is obtained from a preset database. In a specific embodiment, the maximum packet loss rate and the maximum number of retransmissions are set according to the analysis of historical error data. For example, if the maximum value of the packet loss rate in the historical error data does not exceed 0.5%, 0.5% is set as the maximum packet loss rate; if the average number of retransmissions in the historical error data is 2, the maximum number of retransmissions is 2.

[0048] Specifically, the reference connection data is obtained from a preset database. In a specific embodiment, the minimum session duration and the maximum connection frequency are set according to the historical connection data. For example, if the historical session duration is 20 minutes, the minimum session duration can be set to 50% of the historical session duration, that is, 10 minutes; if the historical connection frequency is 5 times / second, the maximum connection frequency can be set to 1.5 times the historical connection frequency, that is, 8 times / second.

[0049] Specifically, the reference abnormal data is obtained from a preset database. In a specific embodiment, the maximum number of abnormal behaviors and the maximum number of access failures are set according to historical abnormal data. For example, if there are at most 3 abnormal behaviors in a historical transmission, 3 can be set as the maximum number of abnormal behaviors; if there are at most 5 access failures in a historical transmission, 5 can be set as the maximum number of access failures.

[0050] Furthermore, the specific process of obtaining the second information leakage risk coefficient is as follows: obtaining a real-time risk assessment weight from a preset database, the real-time risk assessment weight including a first real-time weight and a second real-time weight; obtaining a first real-time risk coefficient (i.e., the second information leakage risk coefficient in the numerical expression) based on the transmission data and the corresponding reference transmission data; ), the first real-time risk coefficient is used to quantify the risk of transmission rate; based on the traffic data and the corresponding reference traffic data, the second real-time risk coefficient (i.e., the second information leakage risk coefficient in the numerical expression) is obtained. ), the second real-time risk coefficient is used to quantify the risk of traffic data; based on the error data and the corresponding reference error data, the third real-time risk coefficient (i.e., the value of the second information leakage risk coefficient in the numerical expression) is obtained. ), the third real-time risk coefficient is used to quantify the risk of erroneous data; the fourth real-time risk coefficient (i.e., the value of the second information leakage risk coefficient in the numerical expression) is obtained based on the connection data and the corresponding reference connection data. ), the fourth real-time risk coefficient is used to quantify the risk of connection duration; the fifth real-time risk coefficient (i.e., the value of the second information leakage risk coefficient in the numerical expression) is obtained based on the abnormal data and the corresponding reference abnormal data. ), the fifth real-time risk coefficient is used to quantify the risk of abnormal activities; the second information leakage risk coefficient is obtained based on the first real-time risk coefficient, the second real-time risk coefficient, the third real-time risk coefficient, the fourth real-time risk coefficient, the fifth real-time risk coefficient, the first information leakage risk coefficient and the real-time risk assessment weight.

[0051] The numerical expression of the second information leakage risk coefficient is as follows:

[0052] ;

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] In the formula, n represents the transmission number of the scientific research data to be transmitted. , Indicates the total number of transmissions of scientific research data to be transmitted, Indicates the transmission speed corresponding to the scientific research data to be transmitted for the nth transmission, Indicates the transmission delay corresponding to the scientific research data to be transmitted for the nth transmission, Indicates the number of data packets corresponding to the scientific research data to be transmitted for the nth transmission, Indicates the transmission flow corresponding to the scientific research data to be transmitted for the nth transmission, Indicates the packet loss rate of the scientific research data to be transmitted for the nth transmission, Indicates the number of retransmissions corresponding to the scientific research data to be transmitted for the nth transmission, Indicates the duration of the session corresponding to the scientific research data to be transmitted for the nth transmission, Indicates the connection frequency corresponding to the scientific research data to be transmitted for the nth transmission, Indicates the number of abnormal behaviors corresponding to the scientific research data to be transmitted for the nth transmission, Indicates the number of failed accesses corresponding to the scientific research data to be transmitted for the nth transmission, Indicates the minimum transmission speed. Indicates the maximum transmission delay. Indicates the maximum transmission flow rate. Indicates the maximum number of data packets. Indicates the maximum packet loss rate. Indicates the maximum number of retransmissions. Indicates the minimum session duration. Indicates the maximum connection frequency. Indicates the maximum number of abnormal behaviors. Indicates the maximum number of failed accesses. represents the first real-time risk factor of the scientific research data to be transmitted for the nth transmission, represents the second real-time risk coefficient of the scientific research data to be transmitted for the nth transmission, represents the third real-time risk factor of the scientific research data to be transmitted for the nth transmission, represents the fourth real-time risk factor of the scientific research data to be transmitted for the nth transmission, represents the fifth real-time risk factor of the scientific research data to be transmitted for the nth transmission, represents the first real-time weight, represents the second real-time weight, represents the first information leakage risk coefficient corresponding to the scientific research data to be transmitted for the nth time, It represents the second information leakage risk coefficient corresponding to the scientific research data to be transmitted for the nth time, and e represents a natural constant.

[0059] In this embodiment, the algorithm combines the first real-time risk coefficient, the second real-time risk coefficient, the third real-time risk coefficient, the fourth real-time risk coefficient, the fifth real-time risk coefficient, the first information leakage risk coefficient and the real-time risk assessment weight for comprehensive analysis to obtain the second information leakage risk coefficient (which increases as these independent variables increase); wherein the first real-time risk coefficient, the second real-time risk coefficient, the third real-time risk coefficient, the fourth real-time risk coefficient and the fifth real-time risk coefficient all represent the numerical value of the impact of the network on information leakage during the transmission process, and together with the first information leakage risk coefficient, they affect the second information leakage risk coefficient. In addition, when the transmission traffic and the number of data packets (corresponding to the variables in the second real-time risk coefficient) increase, it is easy to cause the transmission speed to decrease and the transmission delay to increase (corresponding to the variables in the first real-time risk coefficient), which in turn leads to more packet loss rate and retransmission times (corresponding to the variables in the third real-time risk coefficient). The higher the packet loss rate and the number of retransmission times (corresponding to the variables in the third real-time risk coefficient), the more unstable the session will be, which may cause the network to be unstable. Increasing connection interruptions or extending session duration (corresponding to the variables in the fourth real-time risk coefficient), the increase in the number of abnormal behaviors and the number of access failures (corresponding to the variables in the fifth real-time risk coefficient) often leads to more connection attempts, increasing the connection frequency of sessions (corresponding to the variables in the fourth real-time risk coefficient) and transmission traffic (corresponding to the variables in the second real-time risk coefficient), which may lead to increased packet loss rate and latency. If any of the five real-time risk coefficients is abnormal, it will often trigger a chain reaction of other real-time risk coefficients, gradually amplifying the risks in the network; for example, an increase in the number of abnormal behaviors will lead to a surge in transmission traffic, which will affect the transmission speed and transmission delay, thereby causing an increase in packet loss rate and forming a global negative impact; the larger the value of the second information leakage risk coefficient, the greater the risk of information leakage during real-time transmission. Therefore, real-time monitoring of the network conditions of the scientific research data transmission process can help detect abnormal behaviors in a timely manner, reduce the risk of data information leakage and reduce the loss of scientific research results.

[0060] It should be explained that the first real-time risk coefficient is obtained by comprehensive analysis of the transmission data and the reference transmission data, and the algorithm is divided into two cases for analysis; among them, when the transmission speed is greater than the minimum transmission speed and the transmission delay is less than the maximum transmission delay (i.e. ), as the relative error between the transmission speed and the minimum transmission speed increases (i.e. ), the value of the first real-time risk coefficient gradually decreases, indicating that the risk of information leakage is smaller. Similarly, with the increase of the relative error between the maximum value of the transmission delay and the transmission delay (i.e. ), the value of the first real-time risk coefficient gradually decreases, indicating that the risk of information leakage is smaller; under normal circumstances, the higher the transmission speed, the shorter the transmission delay, and the two jointly affect the result of the first real-time risk coefficient; through the monitoring and analysis of the transmission speed and transmission delay, the impact of the transmission rate on the risk of information leakage is effectively evaluated, which helps to more comprehensively evaluate the risk of information leakage of scientific research data during the transmission process.

[0061] It should be explained that the algorithm of the second real-time risk coefficient is obtained by combining the traffic data and the reference traffic data for comprehensive analysis. The algorithm is divided into two cases for analysis. Among them, when the number of data packets is less than the maximum number of data packets and the transmission flow is less than the maximum transmission flow (i.e. ), then as the relative error between the maximum number of packets and the number of packets increases (i.e. ), the value of the second real-time risk coefficient gradually decreases, indicating that the risk of information leakage is smaller, and as the relative error between the maximum value of the transmission flow and the transmission flow increases (i.e. ), the value of the second real-time risk coefficient gradually decreases, indicating that the risk of information leakage is smaller; under normal circumstances, the larger the transmission flow, the greater the number of data packets transmitted per unit time, and the two together affect the result of the second real-time risk coefficient; therefore, through the monitoring and analysis of the number of data packets and the transmission flow, the impact of traffic data on the risk of information leakage is effectively evaluated, which helps to more comprehensively evaluate the risk of information leakage of scientific research data during the transmission process.

[0062] It should be explained that the algorithm of the third real-time risk coefficient is obtained by combining the error data and the reference error data for comprehensive analysis. The algorithm is divided into two cases for analysis. Among them, when the packet loss rate is less than the maximum packet loss rate and the number of retransmissions is less than the maximum number of retransmissions (i.e. ), as the relative error between the maximum packet loss rate and the packet loss rate increases (i.e. ), the value of the third real-time risk coefficient gradually decreases, indicating that the risk of information leakage is smaller, and as the relative error between the maximum number of retransmissions and the number of retransmissions increases (i.e. ), the value of the third real-time risk coefficient gradually decreases, indicating that the risk of information leakage is smaller; under normal circumstances, a higher packet loss rate will lead to more retransmissions, and the two together affect the result of the third real-time risk coefficient; therefore, through the monitoring and analysis of the packet loss rate and the number of retransmissions, the impact of erroneous data on the risk of information leakage is effectively evaluated, which helps to more comprehensively evaluate the risk of information leakage of scientific research data during the transmission process.

[0063] It should be explained that the algorithm of the fourth real-time risk factor is obtained by combining the connection data and the reference connection data for comprehensive analysis. The algorithm is divided into two cases for analysis. Among them, when the session duration is greater than the minimum session duration or the connection frequency is less than the maximum connection frequency (i.e. ), as the relative error between the session duration and the maximum session duration increases (i.e. ), the value of the fourth real-time risk coefficient gradually increases, indicating that the risk of information leakage is greater, and as the relative error between the maximum value of the connection frequency and the connection frequency increases (i.e. ), the value of the fourth real-time risk coefficient gradually decreases, indicating that the risk of information leakage is smaller; under normal circumstances, the longer the session duration, the lower the connection frequency, and the two together affect the result of the fourth real-time risk coefficient; therefore, through the monitoring and analysis of session duration and connection frequency, the impact of connection data on the risk of information leakage is effectively evaluated, which helps to more comprehensively evaluate the risk of information leakage of scientific research data during the transmission process.

[0064] It should be explained that the algorithm of the fifth real-time risk coefficient is obtained by combining the abnormal data and the reference abnormal data for comprehensive analysis. The algorithm is divided into two cases for analysis. Among them, when the number of abnormal behaviors is less than the maximum number of abnormal behaviors and the number of access failures is less than the maximum number of access failures (i.e. ), then as the relative error between the maximum number of abnormal behaviors and the number of abnormal behaviors increases (i.e. ), the value of the fifth real-time risk coefficient gradually decreases, indicating that the risk of information leakage is smaller, and as the relative error between the maximum number of access failures and the number of access failures increases (i.e. ), the value of the fifth real-time risk coefficient gradually decreases, indicating that the risk of information leakage is smaller; under normal circumstances, more abnormal behaviors tend to lead to more access times, and the two together affect the result of the fifth real-time risk coefficient; therefore, through the monitoring and analysis of the number of abnormal behaviors and the number of access failures, the impact of abnormal data on the risk of information leakage is effectively evaluated, which helps to more comprehensively evaluate the risk of information leakage of scientific research data to be transmitted during the transmission process.

[0065] Specifically, the first real-time weight is the weight corresponding to the first information leakage risk coefficient in the preset database, which represents the numerical value of the influence of the first information leakage risk coefficient on the second information leakage risk coefficient. When used, the weight corresponding to the first information leakage risk coefficient can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the first information leakage risk coefficient corresponding to the scientific research data to be transmitted and the weight corresponding to the first information leakage risk coefficient preset in the preset database form a mapping set, and the real-time first information leakage risk coefficient is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship is a one-to-one correspondence. In this example, its value range is [0, 1].

[0066] Specifically, in this example, the sum of the second real-time weight and the first real-time weight is 1.

[0067] Furthermore, the specific process of determining whether to take transmission warning measures based on the second information leakage risk assessment coefficient and the risk assessment interval is as follows: Compare the second information leakage risk coefficient with the risk assessment interval: If the second information leakage risk coefficient is within the first risk interval, take the first transmission warning measure, which includes interruption measures, recording measures and warning measures. Interruption measures include forced interruption of transmission and disconnection of network connection. Recording measures are used to save detailed information of related abnormal behaviors. Warning measures indicate issuing emergency sound warnings and leakage risk reminders to the preset administrator. Detailed information includes abnormal time, abnormal source and abnormal target. If the second information leakage risk coefficient is within the second risk interval, take the second transmission warning measure, which includes encryption measures and notification measures. Encryption measures indicate forced switching of encryption protocols and increasing the key length of data encryption. Notification measures indicate sending risk notifications to data transmission personnel, data receiving personnel and preset administrators. If the second information leakage risk coefficient is within the third risk interval, no transmission warning measures are taken.

[0068] In this embodiment, the system can flexibly respond to different degrees of information leakage risks according to the degree of risk. This hierarchical response mechanism improves the accuracy and pertinence of the early warning system and avoids excessive warnings or excessive interventions.

[0069] Specifically, the risk assessment interval is obtained from a preset database. In a specific embodiment, all historical information transmission data in which information leakage occurs are substituted into the numerical expression of the second information leakage risk coefficient to obtain the second information leakage risk coefficient and perform a mean operation to obtain a first mean (the value is between 0 and 1), and then all historical information transmission data in which information leakage does not occur are substituted into the numerical expression of the second information leakage risk coefficient to obtain the second information leakage risk coefficient and perform a mean operation to obtain a second mean, then the range from 0 to the second mean is the third risk assessment interval, the range from the second mean to the first mean is the second risk assessment interval, and the range from the first mean to the value 1 is the first risk assessment interval.

[0070] Furthermore, the information reception data includes reception time, number of received data packets, reception delay, and number of honeypot data accesses; the reference reception data includes preset reception time, number of data packets, maximum reception delay, and maximum number of honeypot data accesses; the reception risk threshold is used to determine the degree of information leakage risk during the reception of scientific research data to be transmitted.

[0071] In this embodiment, the receiving time is obtained through the timestamp in the network protocol, the number of received data packets is obtained through the network traffic monitoring tool Wireshark, the receiving delay is obtained through the RTT (Round-Trip Time) in the TCP (Transmission Control Protocol) connection, and the number of honeypot data accesses is obtained through a network honeypot tool (such as Kippo, Dionaea). The honeypot data is data related to the behavior of potential attackers collected by setting up a network "honeypot", usually including the attacker's operation records, attack methods, access paths, tool usage, etc.

[0072] The preset administrator sets the receiving rules according to the actual transmission situation. The receiving rules stipulate the expected receiving time set according to the number of data packets of the scientific research data to be transmitted, that is, the preset receiving time, the maximum value of the receiving delay and the maximum number of honeypot data access times; by monitoring the receiving time, the number of data packets, the receiving delay and the number of honeypot data access times, the risk of information leakage in data reception is evaluated, thereby improving the security and timeliness of the data receiving process.

[0073] Furthermore, the specific process of obtaining the third information leakage risk coefficient is as follows: obtaining the receiving risk assessment weight from the preset database, the receiving risk assessment weight including the first receiving assessment weight, the second receiving risk assessment weight and the third receiving risk assessment weight; obtaining the receiving risk coefficient (i.e. ), the receiving risk coefficient is used to quantify the possibility of receiving anomalies; the third information leakage risk coefficient is obtained based on the receiving risk assessment weight, the first information leakage risk coefficient, the second information leakage risk coefficient and the receiving risk coefficient.

[0074] The numerical expression of the third information leakage risk coefficient is as follows:

[0075] ;

[0076] ;

[0077] In the formula, n represents the transmission number of the scientific research data to be transmitted. , Indicates the total number of transmissions of scientific research data to be transmitted, Indicates the receiving time of the scientific research data to be transmitted for the nth transmission, Indicates the number of received data packets of scientific research data to be transmitted for the nth transmission, represents the reception delay of the scientific research data to be transmitted for the nth transmission, The number of accesses to the honeypot data for the nth transmission of the scientific research data to be transmitted, Indicates the preset receiving time. Indicates the number of packets. Indicates the maximum value of the receiving delay. Indicates the maximum number of honeypot data accesses. represents the first receiving evaluation weight, represents the second receiving risk assessment weight, represents the third receiving risk assessment weight, represents the receiving risk coefficient of the scientific research data to be transmitted for the nth transmission, represents the first information leakage risk coefficient of the scientific research data to be transmitted for the nth time, represents the second information leakage risk coefficient of the scientific research data to be transmitted for the nth transmission, It represents the third information leakage risk coefficient of the scientific research data to be transmitted for the nth time, and e represents a natural constant.

[0078] In this embodiment, the algorithm combines the receiving risk assessment weight, the first information leakage risk coefficient, the second information leakage risk coefficient, the number of received data packets and the receiving risk coefficient for comprehensive analysis to obtain the third information leakage risk coefficient, wherein the analysis of the third information leakage risk coefficient is divided into two cases according to the number of received data packets and the number of data packets; wherein, when the number of received data packets and the number of data packets are equal, as the first information leakage risk coefficient (value between 0 and 1), the second information leakage risk coefficient (value between 0 and 1) and the receiving risk coefficient (value between 0 and 1) increase, the third information leakage risk coefficient also increases accordingly; in combination with the data change table of the third information leakage risk coefficient, a detailed analysis can be performed, assuming that the first receiving assessment weight is 0.2, the second receiving risk assessment weight is 0.3 and the third receiving risk assessment weight is 0.5, as shown in Table 1:

[0079] Table 1 Data changes of the third information leakage risk coefficient:

[0080] ;

[0081] It can be seen from Table 1 that the first information leakage risk coefficient, the second information leakage risk coefficient and the receiving risk coefficient are positively correlated with the third information leakage risk coefficient. For example, for the third row of data and the first row of data, the first information leakage risk coefficient increases from 0.1 in the third row to 0.2 in the first row, the second information leakage risk coefficient increases from 0.2 in the third row to 0.3 in the first row, the receiving risk coefficient increases from 0.3 in the third row to 0.4 in the first row, and the third information leakage risk coefficient increases from 0.23 in the third row to 0.33 in the first row.

[0082] It can be seen from Table 1 that when the first information leakage risk coefficient, the second information leakage risk coefficient and the receiving risk coefficient increase at the same time, the third information leakage risk coefficient will definitely increase. However, due to the influence of the receiving risk assessment weight, when the first information leakage risk coefficient, the second information leakage risk coefficient and the receiving risk coefficient do not increase at the same time, the third information leakage risk coefficient will not necessarily increase. Therefore, when analyzing the third information leakage risk coefficient, the changing trend of the third information leakage risk coefficient must be comprehensively considered in combination with the receiving risk assessment weight, which is more helpful to fully analyze whether the scientific research data to be transmitted is leaked at the receiving end.

[0083] It should be explained that the receiving risk coefficient algorithm combines the receiving time, receiving delay, the number of honeypot data accesses and the reference information data for comprehensive analysis to obtain the receiving risk coefficient, which is divided into two cases; among them, when the receiving delay is less than the maximum receiving delay and the number of honeypot data accesses is less than the maximum number of honeypot data accesses (i.e. ), as the relative error between the maximum receiving delay and the receiving delay increases (i.e. ), the receiving risk coefficient decreases, indicating that the risk of information leakage is smaller, and as the relative error between the maximum number of honeypot data accesses and the number of honeypot data accesses increases (i.e. ), the receiving risk coefficient decreases, indicating that the risk of information leakage is smaller. At the same time, when the receiving time is closer to the preset receiving time, the receiving risk coefficient decreases, that is, the risk of information leakage is smaller. Under normal circumstances, the longer the receiving delay will lead to an increase in the receiving time, and the increase in the number of honeypot data accesses may also lead to a longer receiving delay. The three factors together affect the result of the receiving risk coefficient. The above analysis is conducive to a more objective and comprehensive analysis of whether there is an information leakage risk at the receiving end, thereby ensuring the security of the receiving of the scientific research data to be transmitted.

[0084] Specifically, the first receiving assessment weight is the weight corresponding to the first information leakage risk coefficient in the preset database, which represents the numerical value of the influence of the first information leakage risk coefficient on the third information leakage risk coefficient. When used, the weight corresponding to the first information leakage risk coefficient can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the first information leakage risk coefficient corresponding to the scientific research data to be transmitted and the weight corresponding to the first information leakage risk coefficient preset in the preset database form a mapping set, and the real-time first information leakage risk coefficient is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship is a one-to-one correspondence. In this example, its value range is [0, 1].

[0085] Specifically, the second receiving assessment weight is the weight corresponding to the second information leakage risk coefficient in the preset database, which represents the numerical value of the influence of the second information leakage risk coefficient on the third information leakage risk coefficient. When used, the weight corresponding to the second information leakage risk coefficient can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the second information leakage risk coefficient corresponding to the scientific research data to be transmitted and the weight corresponding to the second information leakage risk coefficient preset in the preset database form a mapping set, and the real-time second information leakage risk coefficient is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship is a one-to-one correspondence. In this example, its value range is [0, 1].

[0086] Specifically, in this example, the sum of the first reception assessment weight, the second reception risk assessment weight, and the third reception risk assessment weight is 1.

[0087] Furthermore, the specific process of determining whether to take receiving warning measures is as follows: compare the third information leakage risk coefficient with the receiving risk threshold: if the third information leakage risk coefficient is not less than the receiving risk threshold, take receiving warning measures, which include terminating receiving measures, blocking measures and risk reminder measures. Terminating receiving measures include stopping data reception and disconnecting the network connection. Blocking measures include blocking the data receiving personnel account and the data transmitting personnel account. Risk reminder measures indicate sending a leakage risk reminder to the preset administrator; if the third information leakage risk coefficient is less than the receiving risk threshold, no receiving warning measures are taken.

[0088] In this embodiment, through the combination of automated judgment, flexible early warning measures and manual notification, an efficient, intelligent and timely security protection effect is achieved. The system can detect and respond to information leakage risks in real time to prevent further spread of data leakage. At the same time, through the design of receiving risk thresholds, frequent false alarms and excessive protection are avoided, ensuring the normal operation of the system in a non-risk state.

[0089] Specifically, the receiving risk threshold is obtained from a preset database. In a specific embodiment, the historical information receiving data corresponding to all information leakage situations at the receiving end is substituted into the numerical expression of the third information leakage risk coefficient and the average value is obtained by performing a mean operation, and the average value is recorded as the receiving risk threshold.

[0090] The embodiment of the present application provides an information leakage risk warning method for scientific research management, including the following steps: judging whether to issue a personnel replacement reminder based on the obtained personnel credit score and the credit score limit; if the personnel replacement reminder is not issued, obtaining a first information leakage risk coefficient based on the personnel credit score and the data score; the personnel credit score indicates that the data transmission personnel and the data receiving personnel are scored for dishonesty through the scientific research dishonesty behavior rules; the personnel credit score includes a first credit score and a second credit score; the data score indicates the result of assigning points to the scientific research data to be transmitted according to the data type; the first information leakage risk coefficient is used to quantify the risk degree of personnel leakage of the scientific research data to be transmitted; the obtained information transmission data, the reference information transmission data and the first information leakage risk coefficient are calculated. The risk coefficient is processed to obtain a second information leakage risk coefficient, and at the same time, based on the second information leakage risk assessment coefficient and the risk assessment interval, it is determined whether to take transmission warning measures. The transmission warning is used to prevent the leakage of scientific research data to be transmitted during the transmission process. The second information leakage risk assessment coefficient is used to quantify the degree of leakage risk of scientific research data to be transmitted during the transmission process. When no transmission warning measures are taken, the third information leakage risk coefficient is obtained by obtaining information receiving data, reference receiving data, the first information leakage risk coefficient and the second information leakage risk coefficient. Based on the third information leakage risk coefficient and the receiving risk threshold, it is determined whether to take receiving warning measures. The third information leakage risk coefficient is used to quantify the degree of leakage risk of scientific research data to be transmitted during the receiving process.

[0091] In this embodiment, the accuracy of risk assessment is refined through multi-dimensional data points and credit points, ensuring that early warning measures are accurate and effective, and preventing the leakage of scientific research data during transmission and reception. This method realizes a comprehensive and accurate leakage risk assessment of scientific research data during the entire transmission and reception process, and has an automated, flexible and efficient early warning mechanism, which helps to ensure the security of scientific research data.

[0092] To summarize, the embodiment of the present application determines whether to issue a personnel replacement reminder through the obtained personnel credit points and credit point limits. If no personnel replacement reminder is issued, it determines whether to take transmission warning measures based on the obtained first information leakage risk coefficient. When no transmission warning measures are taken, it determines whether to take receiving warning measures based on the obtained third information leakage risk coefficient, thereby achieving full-process warning of scientific research data to be transmitted, and further improving the timeliness of information leakage risk warning during data transmission, effectively solving the problem of untimely information leakage risk warning during data transmission in the prior art.

[0093] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0098] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An information leakage risk early warning system for scientific research management, characterized in that: It includes initial risk assessment module, real-time risk assessment module and receiving risk assessment module: Among them, the initial risk assessment module is used to determine whether to issue a personnel replacement reminder based on the obtained personnel credit score and credit score limit. If the personnel replacement reminder is not issued, the first information leakage risk coefficient is obtained based on the personnel credit score and data score. The personnel credit score indicates that the data transmission personnel and the data receiving personnel are scored for dishonesty through the scientific research dishonesty behavior rules. The personnel credit score includes the first credit score and the second credit score. The data score indicates the result of assigning points to the scientific research data to be transmitted according to the data type. The first information leakage risk coefficient is used to quantify the risk level of personnel leakage of the scientific research data to be transmitted; The real-time risk assessment module is used to process the acquired information transmission data, the reference information transmission data and the first information leakage risk coefficient to obtain a second information leakage risk coefficient, and at the same time determine whether to take transmission warning measures based on the second information leakage risk assessment coefficient and the risk assessment interval, and the second information leakage risk assessment coefficient is used to quantify the degree of leakage risk of the scientific research data to be transmitted during the transmission process; The receiving risk assessment module is used to obtain a third information leakage risk coefficient through the acquired information receiving data, reference receiving data, the first information leakage risk coefficient and the second information leakage risk coefficient when no transmission warning measures are taken, and judge whether to take receiving warning measures based on the third information leakage risk coefficient and the receiving risk threshold, and the third information leakage risk coefficient is used to quantify the degree of leakage risk of the scientific research data to be transmitted during the receiving process; The specific process of obtaining the first information leakage risk coefficient is as follows: Separately number the data transmission personnel and data reception personnel, and also number the scientific research data to be transmitted; Obtaining a score evaluation weight from a preset database, wherein the score evaluation weight includes a personnel score weight and a data score weight; The obtained personnel credit points and data points and the corresponding point evaluation weights are processed to obtain a first information leakage risk coefficient, and the numerical expression of the first information leakage risk coefficient is as follows: ； In the formula, n represents the transmission number of the scientific research data to be transmitted. , Indicates the total number of transmissions of scientific research data to be transmitted, Indicates the first credit score of the data transmission personnel, Indicates the second credit score of the data receiver. represents the data integral corresponding to the scientific research data to be transmitted for the nth transmission, represents the personnel score weight, represents the data integration weight, Represents the first information leakage risk coefficient corresponding to the scientific research data to be transmitted for the nth transmission.

2. The information leakage risk early warning system for scientific research management as claimed in claim 1, characterized in that: The initial risk assessment module includes a personnel credit assessment unit, a data risk assessment unit and a transmission risk assessment unit; The personnel credit assessment unit is used to obtain the corresponding personnel credit points according to the dishonest behavior data of the data transmission personnel and the data reception personnel, and when the obtained personnel credit points are not less than the credit point limit, the obtained personnel credit points are transmitted to the data risk assessment unit, otherwise, a personnel replacement reminder is issued; The data risk assessment unit is used to obtain data points according to the transmission type and importance of the scientific research data to be transmitted, and transmit the obtained data points to the sending risk assessment unit, wherein the transmission type includes public data, internal data, sensitive data and highly sensitive data; The sending risk assessment unit is used to obtain a first information leakage risk coefficient based on personnel credit points and data points, and to perform leakage risk assessment on the sending work of the scientific research data to be transmitted.

3. The information leakage risk early warning system for scientific research management as claimed in claim 1, characterized in that: The information transmission data includes transmission data, flow data, error data, connection data and abnormal data; The transmission data includes transmission speed and transmission delay; The traffic data includes transmission traffic and the number of data packets; The error data includes packet loss rate and number of retransmissions; The connection data includes session duration and connection frequency; The abnormal data includes the number of abnormal behaviors and the number of access failures; The reference information transmission data includes reference transmission data, reference flow data, reference error data, reference connection data and reference abnormality data; The reference transmission data includes a minimum transmission speed and a maximum transmission delay; The reference flow data includes the maximum transmission flow and the maximum number of data packets; The reference error data includes a maximum packet loss rate and a maximum number of retransmissions; The reference connection data includes a minimum session duration and a maximum connection frequency; The reference abnormal data includes the maximum number of abnormal behaviors and the maximum number of access failures; The risk assessment intervals include a first risk interval, a second risk interval and a third risk interval.

4. The information leakage risk early warning system for scientific research management as claimed in claim 3, characterized in that: The specific process of obtaining the second information leakage risk coefficient is as follows: Acquire a real-time risk assessment weight from a preset database, wherein the real-time risk assessment weight includes a first real-time weight and a second real-time weight; Obtaining a first real-time risk coefficient based on the transmission data and the corresponding reference transmission data, wherein the first real-time risk coefficient is used to quantify the risk in terms of transmission rate; Obtaining a second real-time risk coefficient based on the flow data and the corresponding reference flow data, wherein the second real-time risk coefficient is used to quantify the risk in the flow data; Obtaining a third real-time risk coefficient based on the error data and the corresponding reference error data, wherein the third real-time risk coefficient is used to quantify the risk of the error data; Obtaining a fourth real-time risk coefficient based on the connection data and the corresponding reference connection data, wherein the fourth real-time risk coefficient is used to quantify the risk in terms of connection duration; obtaining a fifth real-time risk coefficient based on the abnormal data and the corresponding reference abnormal data, wherein the fifth real-time risk coefficient is used to quantify the risk of abnormal activities; A second information leakage risk coefficient is obtained based on the first real-time risk coefficient, the second real-time risk coefficient, the third real-time risk coefficient, the fourth real-time risk coefficient, the fifth real-time risk coefficient, the first information leakage risk coefficient and the real-time risk assessment weight.

5. The information leakage risk warning system for scientific research management as claimed in claim 3, characterized in that: The specific process of determining whether to take transmission warning measures based on the second information leakage risk assessment coefficient and the risk assessment interval is as follows: Compare the second information leakage risk factor with the risk assessment interval: If the second information leakage risk coefficient is within the first risk interval, a first transmission warning measure is taken, wherein the first transmission warning measure includes an interruption measure, a recording measure and a warning measure, wherein the interruption measure includes forcibly interrupting transmission and disconnecting the network connection, the recording measure is used to save detailed information of the relevant abnormal behavior, and the warning measure indicates issuing an emergency sound warning and a leakage risk reminder to a preset administrator; If the second information leakage risk coefficient is within the second risk interval, a second transmission warning measure is taken, and the second transmission warning measure includes an encryption measure and a notification measure. The encryption measure means forcibly switching the encryption protocol and increasing the key length of data encryption, and the notification measure means sending a risk notification to the data transmission personnel, data receiving personnel, and the preset administrator; If the second information leakage risk coefficient is within the third risk range, no transmission warning measures will be taken.

6. The information leakage risk warning system for scientific research management as claimed in claim 1, characterized in that: The information reception data includes reception time, number of received data packets, reception delay, and number of honeypot data accesses; The reference receiving data includes a preset receiving time, a number of data packets, a maximum receiving delay, and a maximum number of honeypot data access times; The receiving risk threshold is used to determine the risk level of information leakage during the process of receiving scientific research data to be transmitted.

7. The information leakage risk warning system for scientific research management as claimed in claim 6, characterized in that: The specific process of obtaining the third information leakage risk coefficient is as follows: Acquire a reception risk assessment weight from a preset database, wherein the reception risk assessment weight includes a first reception risk assessment weight, a second reception risk assessment weight, and a third reception risk assessment weight; Obtaining a receiving risk coefficient according to the receiving time, receiving delay, the number of honeypot data accesses and the reference information data, wherein the receiving risk coefficient is used to quantify the possibility of receiving anomalies; A third information leakage risk coefficient is obtained based on the receiving risk assessment weight, the first information leakage risk coefficient, the second information leakage risk coefficient and the receiving risk coefficient.

8. The information leakage risk warning system for scientific research management as claimed in claim 7, characterized in that: The specific process of determining whether to take the early warning receiving measure is as follows: Compare the third information leakage risk factor with the receiving risk threshold: If the third information leakage risk coefficient is not less than the receiving risk threshold, a receiving warning measure is taken, and the receiving warning measure includes a receiving termination measure, a blocking measure, and a risk reminder measure. The receiving termination measure includes stopping data reception and disconnecting the network connection. The blocking measure includes blocking the data receiving personnel account and the data transmitting personnel account. The risk reminder measure means sending a leakage risk reminder to the preset administrator; If the third information leakage risk coefficient is less than the receiving risk threshold, no receiving warning measures will be taken.

9. A method for applying to an information leakage risk warning system for scientific research management as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: Based on the obtained personnel credit points and credit point limit, determine whether to issue a personnel replacement reminder. If no personnel replacement reminder is issued, obtain a first information leakage risk coefficient based on the personnel credit points and data points. The personnel credit points represent the dishonesty scoring of data transmission personnel and data receiving personnel according to the scientific research dishonesty behavior rules. The personnel credit points include a first credit point and a second credit point. The data points represent the result of assigning points to the scientific research data to be transmitted according to the data type. The first information leakage risk coefficient is used to quantify the risk level of personnel leakage of the scientific research data to be transmitted; The acquired information transmission data, the reference information transmission data and the first information leakage risk coefficient are processed to obtain a second information leakage risk coefficient, and at the same time, based on the second information leakage risk assessment coefficient and the risk assessment interval, it is determined whether to take transmission warning measures, wherein the second information leakage risk assessment coefficient is used to quantify the degree of leakage risk of the scientific research data to be transmitted during the transmission process; When no transmission warning measures are taken, a third information leakage risk coefficient is obtained by obtaining information reception data, reference reception data, the first information leakage risk coefficient and the second information leakage risk coefficient. Based on the third information leakage risk coefficient and the reception risk threshold, it is determined whether to take reception warning measures. The third information leakage risk coefficient is used to quantify the degree of leakage risk of the scientific research data to be transmitted during the reception process.

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